qdrant-search-quality-diagnosis

name

qdrant-search-quality-diagnosis

description

Diagnoses Qdrant search quality issues. Use when someone reports 'results are bad', 'wrong results', 'not relevant results', 'missing matches', 'recall is low', 'approximate search worse than exact', 'which embedding model', 'quality dropped after quantization', 'how to measure retrieval quality', 'build a golden set', 'ground truth dataset', or 'how to score recall@k'. Also use when search quality degrades without obvious changes.

How to Diagnose Bad Search Quality

Before tuning, establish baselines. Use exact KNN as ground truth, compare against approximate HNSW. Target >95% recall@K for production.

Don't Know What's Wrong Yet

Use when: results are irrelevant or missing expected matches and you need to isolate the cause.

• For a no-code quick check, use the Web UI's ANN Recall tab to compare approximate vs exact recall@k Web UI ANN Recall
• For the same comparison in code (CI gating, regression tests), run each query twice — once approximate, once with exact=true — and compute recall@k from the overlap ANN recall in CI
• Exact search bad = model or search pipeline problem. Exact good, approximate bad = tune HNSW.
• Check if quantization degrades quality (compare with and without)
• Check if filters are too restrictive (then you might need to use ACORN)
• If duplicate results from chunked documents, use Grouping API to deduplicate Grouping

Payload filtering and sparse vector search are different things. Metadata (dates, categories, tags) goes in payload for filtering. Text content goes in sparse vectors for search.

Approximate Search Worse Than Exact